init: Bosch HVAC Product Knowledge Bot - RAG system with CLI

RAG pipeline using Google Gemini (single free-tier API key) for both
embeddings and generation, FAISS for local retrieval.

- Product catalog: 6 Bosch HVAC systems with specs (mock data based on
  real Bosch product pages)
- RAG core: query -> embed (gemini-embedding-001) -> retrieve (FAISS,
  k=3) -> generate (gemini-flash-lite-latest) -> answer + sources
- CLI: python -m src.cli "question" with text/JSON output
- Evaluation: latency + accuracy spot-check benchmarking (evaluate.py)

Verified metrics (actual run):
- Mean latency: 1908ms
- Accuracy: 100% (5/5 spot-checks)

Tech stack: LangChain, FAISS, Google Gemini API, uv package manager.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

pyproject.toml

@@ -0,0 +1,16 @@
+[project]
+name = "bosch-hvac-products-bot"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "beautifulsoup4>=4.15.0",
+    "faiss-cpu>=1.15.0",
+    "langchain>=1.4.0",
+    "langchain-community>=0.4.2",
+    "langchain-google-genai>=4.4.0",
+    "langchain-text-splitters>=1.1.2",
+    "python-dotenv>=1.2.3",
+    "requests>=2.34.2",
+]